2020/03/31 by Aurelio F. Bariviera · 32 citations
Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Complex Systems and Time Series Analysis #Computer science #Cryptocurrency #Econometrics #Financial Risk and Volatility Modeling #Fractal #Gaussian #Kurtosis #Mathematics #Multifractal system #Physics #Scaling #Shuffling #Statistical physics #Statistics #Theoretical and Computational Physics #q-fin.GN #q-fin.ST
paper · pdf · doi:10.1016/j.frl.2020.101649
published in Finance research letters 39, 101649 (Elsevier BV)
arxiv created 2020/06/13 · openalex publication_date 2020/06/13 · arxiv updated 2020/06/16 · openalex created_date 2020/06/19 · openalex updated_date 2026/08/05
This paper studies of the multifractal dynamics in 84 cryptocurrencies. It fills an important gap in the literature, by studying this market using two alternative multi-scaling methodologies. We find compelling evidence that cryptocurrencies have different degree of long range dependence, and --more importantly -- follow different stochastic processes. Some of them follow models closer to monofractal fractional Gaussian noises, while others exhibit complex multifractal dynamics. Regarding the source of multifractality, our results are mixed. Time series shuffling produces a reduction in the level of multifractality, but not enough to offset it. We find an association of kurtosis with multifractality.